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Accelerator Portfolio Construction

Accelerator Portfolio Construction MCP Connector for Claude

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Models economic outcomes of venture accelerator programs using power law distributions.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a simulation engine to forecast the economic performance of venture accelerator programs. By applying power law distributions, it models the three primary outcomes of a cohort: failure, small exits, and breakout successes. Users can use simulate_portfolio_outcomes to predict total return multiples and expected value, calculate_reserve_requirements to determine necessary follow-on capital based on graduation velocity, and compare_scenarios to analyze how shifting risk profiles impacts fund performance.

venture-capitalportfolio-modelingpower-laweconomicsrisk-analysis

3 tools expose this connector's capabilities to your AI agent.

calculate_reserve_requirements

Calculates the follow-on capital reserves needed to support breakout companies

compare_scenarios

g., failure or breakout rates) impact the total return multiple. Compares the performance of a base configuration against a modified scenario

simulate_portfolio_outcomes

Simulates the economic outcomes of an accelerator cohort based on power law distributions

See how to talk to your AI agent using Accelerator Portfolio Construction.

What is the expected return for a cohort of 20 companies with a 50% failure rate and a 5% breakout rate, where SaaS has a 10x multiple?

The expected total return multiple for this cohort configuration is 1.45x, with a projected expected value driven primarily by the 5% breakout successes.

How much reserve capital do I need for 30 companies if the breakout rate is 10% and graduation velocity is 0.5?

The required reserve amount is $1,500,000, representing a specific percentage of the total fund to support the expected breakout companies.

Compare a base scenario to one where the breakout rate increases by 2%.

Increasing the breakout rate by 2% results in a delta return of 0.35x compared to the base configuration.

The model uses power law distributions where breakout companies are the primary drivers of the total return multiple. You can use `simulate_portfolio_outcomes` to see how these outliers impact the aggregate cohort value.

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